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1. AML and AMLC Results for OAEI 2019

   Faria, Daniel; Pesquita, Catia; Tervo, Teemu; Couto, Francisco M; Cruz, Isabel F (January 2019, Proceedings of the 14th International Workshop on Ontology Matching co-located with the 18th International Semantic Web Conference (ISWC))

   Shvaiko, Pavel; Euzenat, Jérôme; Jiménez-Ruiz, Ernesto; Hassanzadeh, Oktie; Trojahn, Cássia (Ed.)

   AgreementMakerLight (AML) is an ontology matching system designed with scalability, extensibility and satisfiability as its primary guidelines, as well as an emphasis on the ability to incorporate external knowledge. In OAEI 2019, AML’s development focused mainly on expanding its range of complex matching algorithms, but there were also improvements on its instance matching pipeline and ontology parsing algorithm. AML remains the system with the broadest coverage of OAEI tracks, and among the top performing systems overall. 

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2. Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

   https://doi.org/10.1038/s41540-024-00366-0  

   Katebi, Ataur; Chen, Xiaowen; Ramirez, Daniel; Li, Sheng; Lu, Mingyang (December 2024, npj Systems Biology and Applications)

   Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression. 

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3. Fast Bag-Of-Words Candidate Selection in Content-Based Instance Retrieval Systems

   https://doi.org/10.1109/BigData.2018.8621935  

   Siedlaczek, Michal; Wang, Qi; Chen, Yen-Yu; Suel, Torsten (December 2018, 2018 IEEE International Conference on Big Data)

   Many content-based image search and instance retrieval systems implement bag-of-visual-words strategies for candidate selection. Visual processing of an image results in hundreds of visual words that make up a document, and these words are used to build an inverted index. Query processing then consists of an initial candidate selection phase that queries the inverted index, followed by more complex reranking of the candidates using various image features. The initial phase typically uses disjunctive top-k query processing algorithms originally proposed for searching text collections. Our objective in this paper is to optimize the performance of disjunctive top-k computation for candidate selection in content-based instance retrieval systems. While there has been extensive previous work on optimizing this phase for textual search engines, we are unaware of any published work that studies this problem for instance retrieval, where both index and query data are quite different from the distributions commonly found and exploited in the textual case. Using data from a commercial large-scale instance retrieval system, we address this challenge in three steps. First, we analyze the quantitative properties of index structures and queries in the system, and discuss how they differ from the case of text retrieval. Second, we describe an optimized term-at-a-time retrieval strategy that significantly outperforms baseline term-at-a-time and document-at-a-time strategies, achieving up to 66% speed-up over the most efficient baseline. Finally, we show that due to the different properties of the data, several common safe and unsafe early termination techniques from the literature fail to provide any significant performance benefits. 

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4. Emission Factors From Wildfires in the Western US: An Investigation of Burning State, Ground Versus Air, and Diurnal Dependencies During the FIREX‐AQ 2019 Campaign

   https://doi.org/10.1029/2022JD038460  

   Fiddler, Marc_N; Thompson, Chelia; Pokhrel, Rudra_P; Majluf, Francesca; Canagaratna, Manjula; Fortner, Edward_C; Daube, Conner; Roscioli, Joseph_R; Yacovitch, Tara_I; Herndon, Scott_C; et al (January 2024, Journal of Geophysical Research: Atmospheres)

   Abstract Emission factors (EFs) are crucial in understanding the effects of wildfire emissions on air quality. We examined the variability of EFs of three wildfires (Nethker, Castle, and 204 Cow) during the 2019 Western US wildfire season using the Aerodyne Mobile Laboratory (AML) and compared them to previous studies. The AML sampling captured the high degree of variability present in wildfires, and we report results for a range of combustion conditions that is more extensive than previous field and laboratory studies. For instance, we captured emissions from freshly started flaming fuels and we report rare EF measurements at very high modified combustion efficiencies (MCEs); MCEs >0.9. Differences in emissions between AML‐observed wildfires were attributed to burning state/MCE rather than fuel type. A comparison of EFs versus MCE was made and linear fits were compared to previous observations to reveal important differences that incorporate these high MCEs. For some species, there remains an EF dependence on MCE at these high values, while others reach a minimum value and exhibit either no or a weak dependence above it. EF differences were found for many of the studied compounds when comparing ground‐based and airborne observations, with generally greater airborne EFs possibly due to photochemical oxidation. The largest differences were from monoterpenes and acetaldehyde. Comparisons were made between AML‐observed wildfires, aircraft observations, and the values in literature for EFs and emission ratios, with mixed agreement due to the high degree of variability caused by differences in MCE. Differences in MCE drove the diurnal EF differences. 

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5. Reachability types: tracking aliasing and separation in higher-order functional programs

   https://doi.org/10.1145/3485516  

   Bao, Yuyan; Wei, Guannan; Bračevac, Oliver; Jiang, Yuxuan; He, Qiyang; Rompf, Tiark (October 2021, Proceedings of the ACM on Programming Languages)

   null (Ed.)

   Ownership type systems, based on the idea of enforcing unique access paths, have been primarily focused on objects and top-level classes. However, existing models do not as readily reflect the finer aspects of nested lexical scopes, capturing, or escaping closures in higher-order functional programming patterns, which are increasingly adopted even in mainstream object-oriented languages. We present a new type system, λ * , which enables expressive ownership-style reasoning across higher-order functions. It tracks sharing and separation through reachability sets, and layers additional mechanisms for selectively enforcing uniqueness on top of it. Based on reachability sets, we extend the type system with an expressive flow-sensitive effect system, which enables flavors of move semantics and ownership transfer. In addition, we present several case studies and extensions, including applications to capabilities for algebraic effects, one-shot continuations, and safe parallelization. 

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